Módszerek összehasonlítása

	Robuszt kauzális hatásanalízis	Kauzalitási hatás elemzése
Tudományterület	Oksági következtetés	Oksági következtetés
Módszercsalád	Regression model	Regression model
Keletkezés éve	2015	2015
Megalkotó	Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework)	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
Típus	Bayesian causal inference with robustness validation	Bayesian causal inference / counterfactual forecasting
Alapmű	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.
Alternatív nevek	robust CausalImpact, sensitivity-augmented causal impact, causal impact with robustness checks, robust BSTS causal inference	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
Kapcsolódó	5	5
Összefoglaló	Robust Causal Impact Analysis extends the Bayesian structural time-series CausalImpact framework (Brodersen et al., 2015) by embedding systematic robustness checks — in-time placebo tests, in-space placebo controls, covariate sensitivity analysis, and prior sensitivity assessments — to verify that a detected intervention effect is genuine and not an artifact of model choices or coincidental data patterns.	Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals.